Richard Correro has extensive experience applying statistical modeling, data analysis, and machine learning to solve complex real-world problems. His expertise includes building datasets, developing predictive models, and creating data visualizations, all with the aim of delivering insights to help stakeholders make better, more informed decisions.
At Stanford's Human Trafficking Data Lab, Richard designed computer vision models that are currently being used by public officials in Brazil to identify potential locations of forced labor. He constructed geospatial datasets and used statistical techniques to pinpoint high-risk areas with the goal of better targeting interventions to promote human rights.
For public health initiatives funded by the U.S. State Department, Richard translated objectives into analytical requirements. He built datasets and predictive models to identify regions with increased health complications, helping policy makers to make better, more informed decisions.
With a passion for harnessing data to drive positive social impact, Richard is interested in roles that create public health solutions and enable data-driven policies. His quantitative expertise, socially-conscious mindset, and ability to communicate insights make him a strong fit for analytical positions.
Richard holds an MS in Statistics and a BS in Mathematical and Computational Science from Stanford University.